Publications

Multisensory processing is important for studying and understanding typical and atypical development; however, traditional paradigms involve numerous conditions and trials, making sessions long and tedious. A technique referred to as "continuous-tracking" has been introduced which can assess perceptual thresholds in a shorter time. We tested this technique in an audiovisual context by asking participants to track 1-minute audiovisual stimuli moving in a random walk. The stimuli could be visual, auditory, or audiovisual. In the last case, we had a congruent and incongruent condition with a spatiotemporal shift between the two stimuli, so either vision or audition led the walk by a given time. We further modulated the reliability of the visual stimulus to shift the weight toward the audio. We found a straightforward visual dominance regarding motion perception in audiovisual contexts. Regardless of its state, visual information interferes with auditory perception. Moreover, the continuous tracking yielded a new measurement of motion perception, the lag, giving information on the delay between visual and auditory information processing. Indeed, we observed that the tracking of auditory motion lagged relative to visual motion.

Background/Aims: Metabolic dysfunction-associated steatotic liver disease (MASLD) represents a recent update in defining fatty liver disease, emphasizing its strong connection to metabolic factors and reflecting a shift in understanding its causes and progression. The principal aim of this investigation was to scrutinize the conceivable association between the poverty income ratio (PIR) and the incidence of MASLD, specifically focusing on liver fibrosis. Materials and Methods: In this study, a cross-sectional analysis was carried out utilizing data obtained from the National Health and Nutrition Examination Survey dataset covering the period from 2017 to 2020. To explore the relationship between the PIR and the prevalence of MASLD as well as liver fibrosis, a robust multivariable analytical method was adopted. This approach integrated a wide range of variables, such as sociodemographic characteristics, lifestyle habits, and individual health conditions. Results: In this study, a comprehensive analysis was conducted using logistic regression models and found a significant decline in the likelihood of MASLD in the highest PIR quartile (Q4) (odds ratio [OR] = 0.634, 95% CI: 0.446-0.903, P = .012) as well as liver fibrosis (OR = 0.682, 95% CI: 0.503-0.925, P = .014). Conclusion: The findings obtained from this research strongly demonstrate that higher PIR levels are significantly associated with a reduced prevalence of both MASLD and liver fibrosis, suggesting that higher socioeconomic shighertatus, as reflected by higher PIR, may decrease the risk of these conditions. These findings underscore the need for targeted interventions, such as better nutrition education, lifestyle support, and healthcare access to reduce the MASLD burden in low-income populations.

PURPOSE: The purpose of this study was to evaluate the effectiveness of different machine-learning models in predicting retinal sensitivity in geographic atrophy (GA) secondary to age-related macular degeneration (AMD) and compare the progression of sensitivity loss using observed versus inferred data over time.

METHODS: Thirty patients with GA (37 eyes) were recruited for the OMEGA study. Participants underwent fundus-controlled perimetry (microperimetry) and spectral-domain optical coherence tomography (SD-OCT) imaging at baseline and follow-up visits at weeks 12, 24, and 48. Retinal layers were segmented using a custom-written deep-learning algorithm. We used various machine-learning models, including random forest, LASSO regression, and multivariate adaptive regression splines (MARS), to predict retinal sensitivity across three scenarios: (1) unknown patients, (2) known patients at later visits, and (3) interpolation within visits. Predictive accuracy was evaluated using the mean absolute error (MAE), and the models' ability to reduce test variability over time was analyzed using linear mixed models.

RESULTS: The random forest model demonstrated the highest accuracy across all scenarios, with an MAE of 3.67 decibels (dB) for unknown patients, 2.96 dB for known patients at follow-up, and 3.10 dB for within-visit interpolation. The inferred sensitivity data significantly reduced variability compared to the observed data in longitudinal mixed model analysis, with a residual variance of 2.72 dB² versus 8.67 dB², respectively.

CONCLUSIONS: Machine-learning models, particularly the random forest model, effectively predict retinal sensitivity in patients with GA, with patient-specific baseline data improving accuracy for subsequent visits. Inferred sensitivity mapping presents a reliable, functional surrogate endpoint for clinical trials, offering high spatial resolution without extensive psychophysical testing.

The pupil constricts in response to visual stimuli that keep net luminance unchanged but that do introduce local luminance increments and decrements-a reaction here called "isoluminant constriction." This response can form a pupillometric index of visual processing, but it is unclear what kind of processing it reflects; some authors have suggested that the constriction arises from subcortical, luminance-based neural signals, whereas others have argued for an origin at cortical, feature-based processing stages. We tested the involvement of cortical neural activity in isoluminant constrictions. To this end, we measured constrictions to stimuli presented after contrast adaptation, an adaptation procedure thought to lessen cortical stimulus responses. If cortical processing is involved in the isoluminant constriction, then such adaptation should lead to reduced isoluminant constriction amplitudes. We tested this prediction in the course of three experiments. We found no evidence for the prediction in any of the experiments, and did find Bayesian evidence against the prediction. These results suggest that, at least in the conditions of our experiments, isoluminant constrictions may not reflect visual cortical processing.

PURPOSE: Accurate assessment and surveillance of retinoblastoma (RB) require more efficient and objective measurements. This study aims to develop an artificial intelligence (AI) system, named RB-Care, for automatic classification and quantitative assessment of RB.

METHODS: A total of 3730 wide-field fundus images were included for the development and validation of 2 models in RB-Care. The first model was trained to automatically classify the images into "normal," "unseeded RB," and "seeded RB." The second model performed quantitative assessment on unseeded RB by detecting and segmenting tumors and optic discs.

RESULTS: The classification model of RB-Care can accurately classify fundus images into 3 categories with an accuracy of 0.9734 and an area under the curve (AUC) of 0.9970. The segmentation model can make precise boundary detection and quantitative measurement on tumors and optic discs, achieving mean Intersection over Union (mIoU) of 0.9670 and Dice similarity coefficient (DSC) of 0.9780 for tumor segmentation, and mIoU of 0.9999 and DSC of 0.9999 for optic disc segmentation, which reaches a comparable level with ophthalmologists.

CONCLUSIONS: The RB-Care achieved excellent performance in both RB classification and segmentation. Consequently, the tumor size and the distance between tumor and optic disc can be quantified, which provides an objective measurement tool for quantitative assessment and surveillance of RB in clinical settings.

TRANSLATIONAL RELEVANCE: Developing a clinically relevant technologies for objective quantitative assessment of RB.

The visual system takes advantage of redundancy in the world by extracting summary statistics, a phenomenon known as ensemble perception. Ensemble representations are formed for low-level features like orientation and size and high-level features such as facial identity and expression. Whereas recent research has shown that the visual system forms intact ensemble representations even when faces are partially occluded via solid bars, how ensemble perception is impacted with the addition of naturalistic objects such as face masks or sunglasses is largely unknown. To investigate this, we conducted a series of experiments using continuous report tasks in which faces (either varying in identity or expression) were partially occluded with a surgical mask or sunglasses and participants had to report the average face using a face wheel. We found evidence that participants could still accurately extract the average even when a significant portion of it was occluded with either face masks or sunglasses. In a second experiment, however, we found performance was worse when the face wheel was variable trial to trial. Thus part of the preservation of performance in occlusion arises from the visual system learning the features of the particular face wheel being used. Overall, our results suggest that the visual system is able to establish robust ensemble representations for faces with naturalistic occlusions, but that robustness appears to be supported at least partially by learning information about the particular features that are informative for a given set of faces.

PURPOSE: Glaucoma requires regular visual field (VF) assessments. Eyecatcher 3.0 uses novel "smart glasses" hardware to provide a lightweight, low-cost solution, designed for use while unsupervised. This study aimed to determine the feasibility of using Eyecatcher for VF home-monitoring.

METHODS: Eyecatcher 3.0 consists of a smartphone, smart glasses, and wireless clicker. Functionally, it attempts to mimic the Humphrey Field Analyzer (HFA; - same task-instructions, stimuli, and outputs, but smaller field of view and luminance range). Five patients with glaucoma used Eyecatcher to test themselves at home for 3 months (both eyes, monocular, once-per-fortnight). Results from a reduced 24-2 grid were compared to HFA data collected in the clinic, and to normative Eyecatcher data collected from 76 normally sighted young adults. A subset of normally sighted participants (n = 16) also underwent two additional sessions of follow-up testing to assess repeatability. Usability was assessed via questionnaires.

RESULTS: All Eyecatcher tests were completed successfully (100%). There was reasonable agreement with the HFA in terms of mean deviation (MD; r = 0.85, P < 0.001) and observed pattern of loss. The HFA exhibited somewhat better repeatability than Eyecatcher (MD Coefficient of Repeatability = 2.9, 95% confidence interval [CI] = 2.1-4.1 decibels [dB] for HFA, vs. 3.9, 95% CI = 2.8-6.1 dB for Eyecatcher), although this difference was not statistically significant. Average Eyecatcher test duration was 6.5 minutes (both eyes). Patients generally rated the Eyecatcher as easy-to-use, although specific concerns were raised by some individuals.

CONCLUSIONS: Smart glasses may provide a feasible means of VFs home-monitoring. Eyecatcher yielded similar sensitivity values to the HFA, and most participants found the lightweight smart glasses acceptable to use. Further research is needed to establish diagnostic accuracy and clinical utility.

TRANSLATIONAL RELEVANCE: Validation of a new method of glaucoma home monitoring.

UNLABELLED: The mandible presents morphological variations, even in individuals without syndromes. This variability will determine different skeletal sagittal patterns, generally classified as Class I, II or III. The anatomical position of the mandibular canal has been investigated in different skeletal patterns, often using cone-beam computed tomography (CBCT) images, for diagnostic or surgical planning purposes.

AIM: The aim of this study is to perform a three-dimensional analysis of the position of the mandibular canal (MC) in adults with Class I, II and III skeletal patterns, by means of segmentation and 3D measurements on CBCT images.

MATERIALS AND METHOD: 75 CBCT images were obtained from a secondary database, and 3D analysis was performed using ITK-SNAP and 3D Slicer software. The 3D evaluation consisted of determining the orientation of the position of the mandible, segmentation of the mandible and the MC, creating 3D models, and establishing anatomical landmarks. Vertical (supero-inferior, SI), transverse (mediolateral, RL,) and 3D measurements were performed.

RESULTS: The position of the MC is modified according to the skeletal pattern and by morphological factors of the mandible such as sex and gonial angle. The proximity of the MC to the oblique line is smaller in the SI direction in Class III, and the position of the MC is associated with variation in the gonial angle. It may be closer to the cortical lingual in the central region.

CONCLUSION: The mandibular canal position should be considered in tomographic evaluation during diagnosis and therapeutic planning of mandible surgeries, especially in cases of sagittal ramus osteotomy.

We present data showing that the urinary metabolic ratio (MR) of metabolite to parent drug can be used to estimate the drug-drug interactions (DDIs) of pain management and substance abuse treatment medications with other coadministered drugs. We quantitatively measure 18 drugs and their phase I metabolites and monitor the effects of 14 interfering drugs on their MRs. The 18 drugs include dextromethorphan, oxycodone, hydrocodone, tramadol, morphine, buprenorphine, fentanyl, clonazepam, alprazolam, quetiapine, carisoprodol, tapentadol, ketamine, methadone, impramine, and amitriptyline. The 14 interfering drugs include fluoxetine, paroxetine, bupropion, citalopram, sertraline, venlafaxine, duloxetine, risperidone, trazodone, aripiprazole, cyclobenzaprine, amphetamine, and tetrahydrocannabinol. Some of these interfering drugs are inhibitors of either the CYP2D6, CYP3A4/5, or CYP2C19 pathways. By using the urinary MR of metabolite/parent drug, we observed patterns of inhibition and enhancement due to DDIs. Using the MR reference intervals of the 18 drug pairs established in an earlier study, and the current DDI system, we can alert providers of unusual metabolism caused by DDIs. This will help providers do better prescribing or review more closely all medications and supplements patients are taking, thus avoiding underdosing or potential medication adverse reactions.

We present data that show that quantitative urine drug concentrations obtained from individuals monitored for drug compliance as part of their participation in chronic opioid or substance abuse treatment can be used to quantify drug metabolism. We quantitatively monitor 18 drugs and their Phase 1 metabolite. These drugs were dextromethorphan, morphine, oxycodone, hydrocodone, quetiapine, tapentadol, tramadol, buprenorphine, clonazepam, fentanyl, imipramine, ketamine, carisoprodol, alprazolam, methadone, and amitriptyline. By using the ratio of metabolite/parent drug (prescribed medication), the expected or reference values for 18 drugs were obtained. Ratio values outside of this reference range could be considered to be caused by genetic metabolizing variants, drug-drug interactions, age, or deception. Alerting providers of the variance in metabolism from the expected norm might reduce overdosing or underdosing patients.